{"title":"应用MapReduce编程模型处理科学问题","authors":"Yun-hee Kang, Y. B. Park","doi":"10.1109/ICISA.2014.6847367","DOIUrl":null,"url":null,"abstract":"According to data volumes in scientific applications have grown exponentially, new scientific methods to analyze and organize the data are required. MapReduce programming is driving Internet services and those services operation in a cloud environment. Hence it is required to efficiently provide resources for handling diverse MapReduce applications. In this paper we show the Hadoop application with map and reduce functions for the data transformation.","PeriodicalId":117185,"journal":{"name":"2014 International Conference on Information Science & Applications (ICISA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Applying MapReduce Programming Model for Handling Scientific Problems\",\"authors\":\"Yun-hee Kang, Y. B. Park\",\"doi\":\"10.1109/ICISA.2014.6847367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"According to data volumes in scientific applications have grown exponentially, new scientific methods to analyze and organize the data are required. MapReduce programming is driving Internet services and those services operation in a cloud environment. Hence it is required to efficiently provide resources for handling diverse MapReduce applications. In this paper we show the Hadoop application with map and reduce functions for the data transformation.\",\"PeriodicalId\":117185,\"journal\":{\"name\":\"2014 International Conference on Information Science & Applications (ICISA)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Information Science & Applications (ICISA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISA.2014.6847367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Information Science & Applications (ICISA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISA.2014.6847367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applying MapReduce Programming Model for Handling Scientific Problems
According to data volumes in scientific applications have grown exponentially, new scientific methods to analyze and organize the data are required. MapReduce programming is driving Internet services and those services operation in a cloud environment. Hence it is required to efficiently provide resources for handling diverse MapReduce applications. In this paper we show the Hadoop application with map and reduce functions for the data transformation.
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